Uncovering the causal relationships in plant-microbe ecosystems: A time series analysis of the duckweed cultivation system for biomass production and wastewater treatment.

Abstract

The complex interplay among plants, microbes, and the environment strongly affects productivity of vegetation ecosystems; however, determining causal relationships among various factors in these systems remains challenging. To address this issue, this study aimed to evaluate the potential of a data analytical framework called empirical dynamic modeling, which identifies causal links and directions solely from time series data. By cultivating duckweed, a promising aquatic plant for biomass production and wastewater treatment, we obtained a 63-day time series data of plant productivity, microbial community structure, wastewater treatment performance, and environmental factors. We confirmed that empirical dynamic modeling can identify the correct causal directions among temperature, light intensity and plant growth, solely from time series data. Extending the analysis to microbial community data suggested that the bacterial family Comamonadaceae positively affects host duckweed growth and nitrogen removal. Additionally, the predicted abundance of bacterial genes relevant to xenobiotics biodegradation was shown to have a positive effect on organic pollutant removal, supporting the significant role of bacterial metabolism in phytoremediation performance. These results demonstrate the effectiveness of empirical dynamic modeling in uncovering causal relationships within vegetation ecosystems, which are difficult to examine comprehensively through conventional experiment-based approaches.